APPLICATION OF A COMPUTER TOOL MONITORING SYSTEM IN CNC MACHINING CENTRES
Damian KOLNY
dkolny@ath.bielsko.plUniversity of Bielsko-Biala, Faculty of Mechanical Engineering and Computer Science, 43-309 Bielsko-Biała, Willowa 2 (Poland)
Dorota WIĘCEK
University of Bielsko-Biala, Faculty of Mechanical Engineering and Computer Science, 43-309 Bielsko-Biała, Willowa 2 (Poland)
Paweł ZIOBRO
ZPT Industry | Automation | Research & Development | Innovations (Poland)
Martin KRAJČOVIČ
University of Zilina, Industrial Engineering Department, 010 26 Žilina, Univerzitná 1, (Slovakia)
Abstract
The article presents practical knowledge about production process optimisation as a result of implementing a specialized system monitoring the work of machining tools. It features complex results of the conducted research with use of dedicated equipment and software, whose unconventional application may appear to be an effective IT tool for taking operational and strategic decisions in the machining area. This results from the possibility of analysing the obtained data in both current and long-term perspective, and taking decisions on this basis, which significantly conditions the rationality of using this type of solutions.
Keywords:
current process control, tool wear monitoring system, process optimizationReferences
Addona, D. M. D., & Teti, R. (2013). Image data processing via neural networks for tool wear prediction. Procedia CIRP, 12, 252–257. https://doi.org/10.1016/j.procir.2013.09.044
DOI: https://doi.org/10.1016/j.procir.2013.09.044
Google Scholar
Barreiro, J., Fernández-Abia, A. I., González-Laguna, A., & Pereira, O. (2017). TCM system in contour milling of very thick-very large steel plates based on vibration and AE signals. Journal of Materials Processing Technology, 246, 144–157. https://doi.org/10.1016/j.jmatprotec.2017.03.016
DOI: https://doi.org/10.1016/j.jmatprotec.2017.03.016
Google Scholar
Jemielniak, K. (2002). Automatyczna diagnostyka stanu narzędzia i procesu skrawania. Warszawa: Oficyna Wydawnicza Politechniki Warszawskiej.
Google Scholar
Jurko, J. (2007). Monitoring and Diagnosis of Drill Wear and the Thermodynamic Phenomenas of Material Removal by drilling of Stainless Steels. In: E.E. Gdoutos (Ed.) Experimental Analysis of Nano and Engineering Materials and Structures (vol. 37, 77–78). Dordrecht: Springer. https://doi.org/10.1007/978-1-4020-6239-1_37
DOI: https://doi.org/10.1007/978-1-4020-6239-1_37
Google Scholar
Kious, M., Ouahabi, A., Boudraa, M., Serra, R., & Cheknane, A. (2010). Detection process approach of tool wear in high speed milling. Measurement, 43, 1439–1446. https://doi.org/10.1016/j.measurement.2010.08.014
DOI: https://doi.org/10.1016/j.measurement.2010.08.014
Google Scholar
Kuljanic, E., & Sortino, M. (2005). TWEM a method based on cutting forces monitoring tool wear in face milling, Mach. Tools Manuf. J., 45, 29–34. https://doi.org/10.1016/j.ijmachtools.2004.06.016
DOI: https://doi.org/10.1016/j.ijmachtools.2004.06.016
Google Scholar
Kuryjański, R. (2011). Obróbka skrawaniem i obrabiarki. Warszawa: Expol.
Google Scholar
Nouri, M., Fussell, B. K., Ziniti, B. L., & Linder, E. (2015). Real-time tool wear monitoring in milling using a cutting condition independent method. International Journal of Machine Tools and Manufacture, 89, 1–13. https://doi.org/10.1016/j.ijmachtools.2014.10.011
DOI: https://doi.org/10.1016/j.ijmachtools.2014.10.011
Google Scholar
Storch, B. (2001). Podstawy obróbki skrawaniem. Koszalin: Wydaw. Politechniki Koszalińskiej.
Google Scholar
Więcek, D. (2013). Implementation of Artificial Intelligence in Estimating Prime Costs of Producing Machine Elements. Advances in Manufacturing Science and Technology, 37, 43–53. https://doi.org/10.2478/amst-2013-0004
DOI: https://doi.org/10.2478/amst-2013-0004
Google Scholar
Wittbrodt, P. (2014). Nadzorowanie i prognozowanie stanu narzędzi skrawających w procesie skrawania. Innowacje w Zarządzaniu i Inżynierii Produkcji (cz. 1, 833–834). Zakopane: Oficyna Wydawnicza Polskiego Towarzystwa Zarządzania Produkcją.
Google Scholar
Authors
Damian KOLNYdkolny@ath.bielsko.pl
University of Bielsko-Biala, Faculty of Mechanical Engineering and Computer Science, 43-309 Bielsko-Biała, Willowa 2 Poland
Authors
Dorota WIĘCEK University of Bielsko-Biala, Faculty of Mechanical Engineering and Computer Science, 43-309 Bielsko-Biała, Willowa 2 Poland
Authors
Paweł ZIOBROZPT Industry | Automation | Research & Development | Innovations Poland
Authors
Martin KRAJČOVIČUniversity of Zilina, Industrial Engineering Department, 010 26 Žilina, Univerzitná 1, Slovakia
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